Deep learning is computationally intensive. Model training and model querying have very different computation complexities. A query phase is fast: you apply a function to a vector of input parameters (forward pass), get results.
Model training is much more intensive. Deep learning requires large training datasets in order to produce good results. Datasets with millions of samples are common now, e.g. ImageNet dataset contains over 1 million images. Training is an iterative process: you do forward pass on each sample of the training set, do backward pass to adjust model parameters, repeat the process a few times (epochs). Thus training requires millions, or even billions more computation than one forward pass, and a model can include billions of parameters to adjust.
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It’s interesting that The New York Times published an article about brain cryonics, immortality, connectomics, trans-humanism, and uploading. Kim Suozzi, who died of cancer at age 23, chose to have her brain preserved in hope to get alive sometime in future. One of the options is to scan the brain and map the connections between individual neurons.
“I can see within, say, 40 years that we would have a method to generate a digital replica of a person’s mind,” said Winfried Denk, a director at the Max Planck Institute of Neurobiology in Germany, who has invented one of several mapping techniques.”
“The mapping technique pioneered by Dr. Denk and others involves scanning brains in impossibly thin sheets with an electron microscope. Stacked together on a computer, the scans reveal a three-dimensional map of the connections between each neuron in the tissue, the critical brain anatomy known as the connectome.”
The author doesn’t dive into details of reconstructing a map of neuron connections, though. As Yan LeCunn points out, “connectomics efforts use 3D convolutional nets to analyze the volumetric brain images and to reconstruct the neural circuits.”
As strange as it may sound, neuroscientists use artificial neural networks to reconstruct models of human neural networks. Yet another good use of deep learning techniques.